Overview

Dataset statistics

Number of variables15
Number of observations726498
Missing cells1129255
Missing cells (%)10.4%
Duplicate rows79
Duplicate rows (%)< 0.1%
Total size in memory83.1 MiB
Average record size in memory120.0 B

Variable types

Numeric10
Text4
DateTime1

Alerts

Dataset has 79 (< 0.1%) duplicate rowsDuplicates
Active is highly overall correlated with Confirmed and 1 other fieldsHigh correlation
Case_Fatality_Ratio is highly overall correlated with letalidadHigh correlation
Confirmed is highly overall correlated with Active and 2 other fieldsHigh correlation
Deaths is highly overall correlated with Active and 1 other fieldsHigh correlation
Incident_Rate is highly overall correlated with RecoveredHigh correlation
Long_ is highly overall correlated with RecoveredHigh correlation
Recovered is highly overall correlated with Confirmed and 2 other fieldsHigh correlation
letalidad is highly overall correlated with Case_Fatality_RatioHigh correlation
FIPS has 135366 (18.6%) missing valuesMissing
Admin2 has 134461 (18.5%) missing valuesMissing
Province_State has 32379 (4.5%) missing valuesMissing
Lat has 16276 (2.2%) missing valuesMissing
Long_ has 16276 (2.2%) missing valuesMissing
Recovered has 380080 (52.3%) missing valuesMissing
Active has 380080 (52.3%) missing valuesMissing
Incident_Rate has 16276 (2.2%) missing valuesMissing
Case_Fatality_Ratio has 8394 (1.2%) missing valuesMissing
letalidad has 9667 (1.3%) missing valuesMissing
Active is highly skewed (γ1 = 27.11903144)Skewed
Case_Fatality_Ratio is highly skewed (γ1 = 71.75668861)Skewed
letalidad is highly skewed (γ1 = 67.82994021)Skewed
Confirmed has 9667 (1.3%) zerosZeros
Deaths has 42270 (5.8%) zerosZeros
Recovered has 236816 (32.6%) zerosZeros
Active has 12273 (1.7%) zerosZeros
Case_Fatality_Ratio has 35337 (4.9%) zerosZeros
letalidad has 33824 (4.7%) zerosZeros

Reproduction

Analysis started2025-11-26 01:30:22.891037
Analysis finished2025-11-26 01:31:07.634120
Duration44.74 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

FIPS
Real number (ℝ)

Missing 

Distinct3266
Distinct (%)0.6%
Missing135366
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean32400.145
Minimum66
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:07.827328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile5097
Q119049
median30067
Q347041
95-th percentile56009
Maximum99999
Range99933
Interquartile range (IQR)27992

Descriptive statistics

Standard deviation18029.785
Coefficient of variation (CV)0.55647236
Kurtosis0.44926457
Mean32400.145
Median Absolute Deviation (MAD)12904
Skewness0.57306015
Sum1.9152763 × 1010
Variance3.2507316 × 108
MonotonicityNot monotonic
2025-11-26T01:31:08.009003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39163181
 
< 0.1%
39165181
 
< 0.1%
39167181
 
< 0.1%
39169181
 
< 0.1%
39171181
 
< 0.1%
39173181
 
< 0.1%
39175181
 
< 0.1%
40001181
 
< 0.1%
40003181
 
< 0.1%
40005181
 
< 0.1%
Other values (3256)589322
81.1%
(Missing)135366
 
18.6%
ValueCountFrequency (%)
66181
< 0.1%
69181
< 0.1%
78181
< 0.1%
1001181
< 0.1%
1003181
< 0.1%
1005181
< 0.1%
1007181
< 0.1%
1009181
< 0.1%
1011181
< 0.1%
1013181
< 0.1%
ValueCountFrequency (%)
99999181
< 0.1%
90056181
< 0.1%
90055181
< 0.1%
90054181
< 0.1%
90053181
< 0.1%
90051181
< 0.1%
90050181
< 0.1%
90049181
< 0.1%
90048181
< 0.1%
90047181
< 0.1%

Admin2
Text

Missing 

Distinct1926
Distinct (%)0.3%
Missing134461
Missing (%)18.5%
Memory size5.5 MiB
2025-11-26T01:31:08.294734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length35
Mean length7.1445602
Min length3

Characters and Unicode

Total characters4229844
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutauga
2nd rowBaldwin
3rd rowBarbour
4th rowBibb
5th rowBlount
ValueCountFrequency (%)
unassigned9231
 
1.4%
washington5430
 
0.8%
jefferson5068
 
0.8%
franklin4706
 
0.7%
st4706
 
0.7%
lincoln4344
 
0.7%
jackson4344
 
0.7%
san3801
 
0.6%
madison3620
 
0.6%
of3425
 
0.5%
Other values (1951)594738
92.4%
2025-11-26T01:31:08.668771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a423540
 
10.0%
e402001
 
9.5%
n359828
 
8.5%
o320356
 
7.6%
r280550
 
6.6%
l234576
 
5.5%
i226069
 
5.3%
s200186
 
4.7%
t188226
 
4.4%
u108043
 
2.6%
Other values (47)1486469
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4229844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a423540
 
10.0%
e402001
 
9.5%
n359828
 
8.5%
o320356
 
7.6%
r280550
 
6.6%
l234576
 
5.5%
i226069
 
5.3%
s200186
 
4.7%
t188226
 
4.4%
u108043
 
2.6%
Other values (47)1486469
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4229844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a423540
 
10.0%
e402001
 
9.5%
n359828
 
8.5%
o320356
 
7.6%
r280550
 
6.6%
l234576
 
5.5%
i226069
 
5.3%
s200186
 
4.7%
t188226
 
4.4%
u108043
 
2.6%
Other values (47)1486469
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4229844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a423540
 
10.0%
e402001
 
9.5%
n359828
 
8.5%
o320356
 
7.6%
r280550
 
6.6%
l234576
 
5.5%
i226069
 
5.3%
s200186
 
4.7%
t188226
 
4.4%
u108043
 
2.6%
Other values (47)1486469
35.1%

Province_State
Text

Missing 

Distinct597
Distinct (%)0.1%
Missing32379
Missing (%)4.5%
Memory size5.5 MiB
2025-11-26T01:31:08.922402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length32
Mean length8.5293617
Min length3

Characters and Unicode

Total characters5920392
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralian Capital Territory
2nd rowNew South Wales
3rd rowNorthern Territory
4th rowQueensland
5th rowSouth Australia
ValueCountFrequency (%)
texas46155
 
5.5%
virginia34390
 
4.1%
georgia29141
 
3.5%
north28236
 
3.4%
carolina26788
 
3.2%
new24254
 
2.9%
kentucky21901
 
2.6%
dakota21901
 
2.6%
missouri21177
 
2.5%
south20996
 
2.5%
Other values (660)566691
67.3%
2025-11-26T01:31:09.580033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a790031
13.3%
i589153
 
10.0%
n475650
 
8.0%
o464084
 
7.8%
s447764
 
7.6%
e356389
 
6.0%
r315483
 
5.3%
t213397
 
3.6%
l204345
 
3.5%
147511
 
2.5%
Other values (50)1916585
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5920392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a790031
13.3%
i589153
 
10.0%
n475650
 
8.0%
o464084
 
7.8%
s447764
 
7.6%
e356389
 
6.0%
r315483
 
5.3%
t213397
 
3.6%
l204345
 
3.5%
147511
 
2.5%
Other values (50)1916585
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5920392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a790031
13.3%
i589153
 
10.0%
n475650
 
8.0%
o464084
 
7.8%
s447764
 
7.6%
e356389
 
6.0%
r315483
 
5.3%
t213397
 
3.6%
l204345
 
3.5%
147511
 
2.5%
Other values (50)1916585
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5920392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a790031
13.3%
i589153
 
10.0%
n475650
 
8.0%
o464084
 
7.8%
s447764
 
7.6%
e356389
 
6.0%
r315483
 
5.3%
t213397
 
3.6%
l204345
 
3.5%
147511
 
2.5%
Other values (50)1916585
32.4%
Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:09.727568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length2
Mean length2.914114
Min length2

Characters and Unicode

Total characters2117098
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
us593123
80.1%
russia15023
 
2.0%
japan8869
 
1.2%
india6697
 
0.9%
china6154
 
0.8%
colombia6154
 
0.8%
mexico5973
 
0.8%
ukraine5068
 
0.7%
brazil4887
 
0.7%
peru4706
 
0.6%
Other values (225)83419
 
11.3%
2025-11-26T01:31:09.991311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S605793
28.6%
U602354
28.5%
a145683
 
6.9%
i96071
 
4.5%
n76541
 
3.6%
e62604
 
3.0%
s48126
 
2.3%
r40703
 
1.9%
o37809
 
1.8%
l37286
 
1.8%
Other values (50)364128
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2117098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S605793
28.6%
U602354
28.5%
a145683
 
6.9%
i96071
 
4.5%
n76541
 
3.6%
e62604
 
3.0%
s48126
 
2.3%
r40703
 
1.9%
o37809
 
1.8%
l37286
 
1.8%
Other values (50)364128
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2117098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S605793
28.6%
U602354
28.5%
a145683
 
6.9%
i96071
 
4.5%
n76541
 
3.6%
e62604
 
3.0%
s48126
 
2.3%
r40703
 
1.9%
o37809
 
1.8%
l37286
 
1.8%
Other values (50)364128
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2117098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S605793
28.6%
U602354
28.5%
a145683
 
6.9%
i96071
 
4.5%
n76541
 
3.6%
e62604
 
3.0%
s48126
 
2.3%
r40703
 
1.9%
o37809
 
1.8%
l37286
 
1.8%
Other values (50)364128
17.2%
Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Minimum2020-08-04 02:27:56
Maximum2021-07-01 04:21:19
Invalid dates0
Invalid dates (%)0.0%
2025-11-26T01:31:10.113800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:10.260680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Lat
Real number (ℝ)

Missing 

Distinct3924
Distinct (%)0.6%
Missing16276
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean35.749866
Minimum-71.9499
Maximum71.7069
Zeros0
Zeros (%)0.0%
Negative22623
Negative (%)3.1%
Memory size5.5 MiB
2025-11-26T01:31:10.412495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-71.9499
5-th percentile9.3373
Q133.200697
median37.901449
Q342.183322
95-th percentile49.448196
Maximum71.7069
Range143.6568
Interquartile range (IQR)8.9826252

Descriptive statistics

Standard deviation13.416706
Coefficient of variation (CV)0.37529388
Kurtosis9.8510168
Mean35.749866
Median Absolute Deviation (MAD)4.4823758
Skewness-2.5033854
Sum25390341
Variance180.008
MonotonicityNot monotonic
2025-11-26T01:31:10.535248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.9399362
 
< 0.1%
36.4017975181
 
< 0.1%
36.41689489181
 
< 0.1%
35.48665845181
 
< 0.1%
35.20383864181
 
< 0.1%
35.87147226181
 
< 0.1%
34.98960527181
 
< 0.1%
36.36880673181
 
< 0.1%
35.78879266181
 
< 0.1%
36.39728256181
 
< 0.1%
Other values (3914)708231
97.5%
(Missing)16276
 
2.2%
ValueCountFrequency (%)
-71.9499181
< 0.1%
-52.368181
< 0.1%
-51.7963181
< 0.1%
-45.9864181
< 0.1%
-42.8821181
< 0.1%
-41.9198181
< 0.1%
-40.9006181
< 0.1%
-40.231181
< 0.1%
-38.9489181
< 0.1%
-38.4161181
< 0.1%
ValueCountFrequency (%)
71.7069181
< 0.1%
70.2998181
< 0.1%
69.31479216181
< 0.1%
68.27557185181
< 0.1%
68.0000418181
< 0.1%
67.1471631181
< 0.1%
67.04919196181
< 0.1%
66.941626181
< 0.1%
66.8309181
< 0.1%
66.0006475181
< 0.1%

Long_
Real number (ℝ)

High correlation  Missing 

Distinct3915
Distinct (%)0.6%
Missing16276
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean-71.090433
Minimum-178.1165
Maximum178.065
Zeros0
Zeros (%)0.0%
Negative624267
Negative (%)85.9%
Memory size5.5 MiB
2025-11-26T01:31:10.671761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-178.1165
5-th percentile-116.46302
Q1-96.595639
median-86.717326
Q3-77.3579
95-th percentile73.121219
Maximum178.065
Range356.1815
Interquartile range (IQR)19.237739

Descriptive statistics

Standard deviation55.327006
Coefficient of variation (CV)-0.77826232
Kurtosis5.2189961
Mean-71.090433
Median Absolute Deviation (MAD)9.6234762
Skewness2.3699265
Sum-50489990
Variance3061.0775
MonotonicityNot monotonic
2025-11-26T01:31:10.801561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-36.9541362
 
< 0.1%
-75.5277362
 
< 0.1%
-72.3311362
 
< 0.1%
-73.6536362
 
< 0.1%
-76.826362
 
< 0.1%
-72.1416362
 
< 0.1%
-74.03362
 
< 0.1%
-70.812362
 
< 0.1%
-101.7068362
 
< 0.1%
-36.782362
 
< 0.1%
Other values (3905)706602
97.3%
(Missing)16276
 
2.2%
ValueCountFrequency (%)
-178.1165179
< 0.1%
-175.1982181
< 0.1%
-174.1596181
< 0.1%
-172.1046181
< 0.1%
-169.8672181
< 0.1%
-168.734181
< 0.1%
-164.0353804181
< 0.1%
-163.3967883181
< 0.1%
-162.8905196181
< 0.1%
-161.9722021181
< 0.1%
ValueCountFrequency (%)
178.065181
< 0.1%
177.6493181
< 0.1%
174.886181
< 0.1%
171.1845181
< 0.1%
169.4900869181
< 0.1%
166.9592181
< 0.1%
166.9315181
< 0.1%
165.618042181
< 0.1%
160.1562181
< 0.1%
160.0383819181
< 0.1%

Confirmed
Real number (ℝ)

High correlation  Zeros 

Distinct102277
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33495.735
Minimum0
Maximum6061404
Zeros9667
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:10.930677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165
Q11012
median2819
Q311280
95-th percentile111987.45
Maximum6061404
Range6061404
Interquartile range (IQR)10268

Descriptive statistics

Standard deviation184050.55
Coefficient of variation (CV)5.4947458
Kurtosis365.13131
Mean33495.735
Median Absolute Deviation (MAD)2292
Skewness16.395751
Sum2.4334585 × 1010
Variance3.3874605 × 1010
MonotonicityNot monotonic
2025-11-26T01:31:11.059563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09667
 
1.3%
1748
 
0.1%
20557
 
0.1%
54503
 
0.1%
4493
 
0.1%
53450
 
0.1%
13424
 
0.1%
267407
 
0.1%
712389
 
0.1%
49376
 
0.1%
Other values (102267)712484
98.1%
ValueCountFrequency (%)
09667
1.3%
1748
 
0.1%
2233
 
< 0.1%
3228
 
< 0.1%
4493
 
0.1%
551
 
< 0.1%
622
 
< 0.1%
731
 
< 0.1%
8374
 
0.1%
9340
 
< 0.1%
ValueCountFrequency (%)
60614041
< 0.1%
60516331
< 0.1%
60435481
< 0.1%
60368211
< 0.1%
60268471
< 0.1%
60170351
< 0.1%
60074311
< 0.1%
59975871
< 0.1%
59875211
< 0.1%
59790511
< 0.1%

Deaths
Real number (ℝ)

High correlation  Zeros 

Distinct17344
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.00375
Minimum0
Maximum133398
Zeros42270
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:11.190229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median50
Q3175
95-th percentile2760
Maximum133398
Range133398
Interquartile range (IQR)159

Descriptive statistics

Standard deviation4400.4862
Coefficient of variation (CV)5.9225626
Kurtosis356.94872
Mean743.00375
Median Absolute Deviation (MAD)43
Skewness16.462356
Sum5.3979074 × 108
Variance19364279
MonotonicityNot monotonic
2025-11-26T01:31:11.324613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042270
 
5.8%
211458
 
1.6%
310882
 
1.5%
110833
 
1.5%
129897
 
1.4%
49859
 
1.4%
69693
 
1.3%
99322
 
1.3%
79061
 
1.2%
118545
 
1.2%
Other values (17334)594678
81.9%
ValueCountFrequency (%)
042270
5.8%
110833
 
1.5%
211458
 
1.6%
310882
 
1.5%
49859
 
1.4%
58290
 
1.1%
69693
 
1.3%
79061
 
1.2%
88133
 
1.1%
99322
 
1.3%
ValueCountFrequency (%)
1333981
< 0.1%
1333751
< 0.1%
1333541
< 0.1%
1333411
< 0.1%
1333211
< 0.1%
1333111
< 0.1%
1332901
< 0.1%
1332671
< 0.1%
1332611
< 0.1%
1332481
< 0.1%

Recovered
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct59579
Distinct (%)17.2%
Missing380080
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean41452.59
Minimum0
Maximum5819901
Zeros236816
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:11.467297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32057
95-th percentile202525.75
Maximum5819901
Range5819901
Interquartile range (IQR)2057

Descriptive statistics

Standard deviation207937.25
Coefficient of variation (CV)5.0162668
Kurtosis199.39807
Mean41452.59
Median Absolute Deviation (MAD)0
Skewness11.703451
Sum1.4359923 × 1010
Variance4.32379 × 1010
MonotonicityNot monotonic
2025-11-26T01:31:11.596891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236816
32.6%
1495
 
0.1%
13411
 
0.1%
4391
 
0.1%
18249
 
< 0.1%
7209
 
< 0.1%
183202
 
< 0.1%
98202
 
< 0.1%
977189
 
< 0.1%
699189
 
< 0.1%
Other values (59569)107065
 
14.7%
(Missing)380080
52.3%
ValueCountFrequency (%)
0236816
32.6%
1495
 
0.1%
2150
 
< 0.1%
3135
 
< 0.1%
4391
 
0.1%
530
 
< 0.1%
611
 
< 0.1%
7209
 
< 0.1%
8182
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
58199011
< 0.1%
58095481
< 0.1%
58009251
< 0.1%
57901131
< 0.1%
57815511
< 0.1%
57727991
< 0.1%
57626611
< 0.1%
57532901
< 0.1%
57422581
< 0.1%
57332151
< 0.1%

Active
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct54250
Distinct (%)15.7%
Missing380080
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean18336.113
Minimum0
Maximum5431304
Zeros12273
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:11.724600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q1616
median2043
Q37588
95-th percentile62870.35
Maximum5431304
Range5431304
Interquartile range (IQR)6972

Descriptive statistics

Standard deviation135814.83
Coefficient of variation (CV)7.4069589
Kurtosis855.29412
Mean18336.113
Median Absolute Deviation (MAD)1811
Skewness27.119031
Sum6.3519595 × 109
Variance1.8445669 × 1010
MonotonicityNot monotonic
2025-11-26T01:31:11.857304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012273
 
1.7%
11339
 
0.2%
2892
 
0.1%
3696
 
0.1%
8641
 
0.1%
4623
 
0.1%
6548
 
0.1%
5441
 
0.1%
7439
 
0.1%
10437
 
0.1%
Other values (54240)328089
45.2%
(Missing)380080
52.3%
ValueCountFrequency (%)
012273
1.7%
11339
 
0.2%
2892
 
0.1%
3696
 
0.1%
4623
 
0.1%
5441
 
0.1%
6548
 
0.1%
7439
 
0.1%
8641
 
0.1%
9419
 
0.1%
ValueCountFrequency (%)
54313041
< 0.1%
54145491
< 0.1%
54000941
< 0.1%
53977881
< 0.1%
53842361
< 0.1%
53696081
< 0.1%
53640831
< 0.1%
53458201
< 0.1%
53274441
< 0.1%
53080001
< 0.1%
Distinct4014
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:12.095087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length45
Mean length20.426023
Min length4

Characters and Unicode

Total characters14839465
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
us593123
26.7%
texas46517
 
2.1%
virginia34571
 
1.6%
georgia29322
 
1.3%
north28960
 
1.3%
carolina26969
 
1.2%
new26245
 
1.2%
dakota22263
 
1.0%
kentucky21901
 
1.0%
south21539
 
1.0%
Other values (2756)1373525
61.7%
2025-11-26T01:31:12.492273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1498437
 
10.1%
a1359254
 
9.2%
,1286880
 
8.7%
n912019
 
6.1%
i911293
 
6.1%
o822249
 
5.5%
e820994
 
5.5%
s696076
 
4.7%
S690487
 
4.7%
r636736
 
4.3%
Other values (52)5205040
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14839465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1498437
 
10.1%
a1359254
 
9.2%
,1286880
 
8.7%
n912019
 
6.1%
i911293
 
6.1%
o822249
 
5.5%
e820994
 
5.5%
s696076
 
4.7%
S690487
 
4.7%
r636736
 
4.3%
Other values (52)5205040
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14839465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1498437
 
10.1%
a1359254
 
9.2%
,1286880
 
8.7%
n912019
 
6.1%
i911293
 
6.1%
o822249
 
5.5%
e820994
 
5.5%
s696076
 
4.7%
S690487
 
4.7%
r636736
 
4.3%
Other values (52)5205040
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14839465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1498437
 
10.1%
a1359254
 
9.2%
,1286880
 
8.7%
n912019
 
6.1%
i911293
 
6.1%
o822249
 
5.5%
e820994
 
5.5%
s696076
 
4.7%
S690487
 
4.7%
r636736
 
4.3%
Other values (52)5205040
35.1%

Incident_Rate
Real number (ℝ)

High correlation  Missing 

Distinct484787
Distinct (%)68.3%
Missing16276
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean7995.4699
Minimum0
Maximum41459.613
Zeros2830
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:12.615299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile444.90738
Q15563.7763
median8479.8576
Q310608.422
95-th percentile13638.52
Maximum41459.613
Range41459.613
Interquartile range (IQR)5044.6461

Descriptive statistics

Standard deviation3946.6369
Coefficient of variation (CV)0.49360913
Kurtosis0.97089128
Mean7995.4699
Median Absolute Deviation (MAD)2431.2277
Skewness0.0065607373
Sum5.6785586 × 109
Variance15575943
MonotonicityNot monotonic
2025-11-26T01:31:12.748840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02830
 
0.4%
0.3038509564181
 
< 0.1%
43.69747899181
 
< 0.1%
0.02741152929181
 
< 0.1%
6.847790732181
 
< 0.1%
3.790758633181
 
< 0.1%
0.8521079871181
 
< 0.1%
70.65889419181
 
< 0.1%
0.3812028313181
 
< 0.1%
3337.453646181
 
< 0.1%
Other values (484777)705763
97.1%
(Missing)16276
 
2.2%
ValueCountFrequency (%)
02830
0.4%
0.02741152929181
 
< 0.1%
0.3038509564181
 
< 0.1%
0.341670083466
 
< 0.1%
0.3812028313181
 
< 0.1%
0.48719237969
 
< 0.1%
0.489310607348
 
< 0.1%
0.49354706283
 
< 0.1%
0.495665290520
 
< 0.1%
0.49778351838
 
< 0.1%
ValueCountFrequency (%)
41459.613091
 
< 0.1%
41367.928851
 
< 0.1%
41111.212981
 
< 0.1%
41102.044565
< 0.1%
40918.676081
 
< 0.1%
40496.928584
< 0.1%
40414.412762
 
< 0.1%
40405.244343
< 0.1%
40396.075911
 
< 0.1%
39974.328411
 
< 0.1%

Case_Fatality_Ratio
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct305215
Distinct (%)42.5%
Missing8394
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.6825172
Minimum0
Maximum4965.3061
Zeros35337
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:12.884967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.058685446
Q11.1510791
median1.7356969
Q32.4649144
95-th percentile4.0650407
Maximum4965.3061
Range4965.3061
Interquartile range (IQR)1.3138353

Descriptive statistics

Standard deviation41.759094
Coefficient of variation (CV)15.56713
Kurtosis5770.0481
Mean2.6825172
Median Absolute Deviation (MAD)0.64130565
Skewness71.756689
Sum1926326.4
Variance1743.8219
MonotonicityNot monotonic
2025-11-26T01:31:13.028372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035337
 
4.9%
1.886792453618
 
0.1%
1.960784314589
 
0.1%
1.923076923473
 
0.1%
1.818181818428
 
0.1%
2419
 
0.1%
1.694915254395
 
0.1%
2.777777778378
 
0.1%
2.127659574348
 
< 0.1%
1.639344262346
 
< 0.1%
Other values (305205)678773
93.4%
(Missing)8394
 
1.2%
ValueCountFrequency (%)
035337
4.9%
0.014838682331
 
< 0.1%
0.014843402111
 
< 0.1%
0.014843716873
 
< 0.1%
0.01484560571
 
< 0.1%
0.014847495021
 
< 0.1%
0.014847809953
 
< 0.1%
0.014848124892
 
< 0.1%
0.014848439852
 
< 0.1%
0.014850960011
 
< 0.1%
ValueCountFrequency (%)
4965.3061221
 
< 0.1%
48663
< 0.1%
4770.5882355
< 0.1%
4590.5660381
 
< 0.1%
4505.5555563
< 0.1%
3425.3521131
 
< 0.1%
3151.9480521
 
< 0.1%
3076.7123291
 
< 0.1%
3023.751
 
< 0.1%
3021.251
 
< 0.1%

letalidad
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct306936
Distinct (%)42.8%
Missing9667
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2.6607663
Minimum0
Maximum4975.5102
Zeros33824
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-11-26T01:31:13.169786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12188929
Q11.1541525
median1.7346054
Q32.4546833
95-th percentile4.0682601
Maximum4975.5102
Range4975.5102
Interquartile range (IQR)1.3005308

Descriptive statistics

Standard deviation39.690771
Coefficient of variation (CV)14.917045
Kurtosis4968.6318
Mean2.6607663
Median Absolute Deviation (MAD)0.63397645
Skewness67.82994
Sum1907319.8
Variance1575.3573
MonotonicityNot monotonic
2025-11-26T01:31:13.312321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033824
 
4.7%
1.886792453597
 
0.1%
1.960784314583
 
0.1%
1.818181818415
 
0.1%
2.777777778402
 
0.1%
2398
 
0.1%
1.923076923390
 
0.1%
2.325581395378
 
0.1%
1.694915254360
 
< 0.1%
1.351351351355
 
< 0.1%
Other values (306926)679129
93.5%
(Missing)9667
 
1.3%
ValueCountFrequency (%)
033824
4.7%
0.0084792470431
 
< 0.1%
0.0084819440621
 
< 0.1%
0.0084821239243
 
< 0.1%
0.0084832032581
 
< 0.1%
0.0084842828661
 
< 0.1%
0.0084844628273
 
< 0.1%
0.0084846427972
 
< 0.1%
0.0084848227732
 
< 0.1%
0.0084862628621
 
< 0.1%
ValueCountFrequency (%)
4975.5102041
< 0.1%
4615.094341
< 0.1%
4529.629631
< 0.1%
4505.5555562
< 0.1%
3425.3521131
< 0.1%
3151.9480521
< 0.1%
3076.7123291
< 0.1%
3023.751
< 0.1%
3021.251
< 0.1%
3011.251
< 0.1%

Interactions

2025-11-26T01:31:00.417583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:43.527303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:45.272578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.083429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:48.908578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:50.911533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:52.734700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:54.783355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:56.431161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:58.524166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:00.688913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:43.681271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:45.441017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.287371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.096541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.093914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:52.903938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:54.973556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:56.883897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:58.711094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:00.975770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:43.835554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:45.624758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.464459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.294260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.282279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:53.134012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.189703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.065424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:58.914972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:01.258802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:43.997118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:45.810431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.641684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.466679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.480750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:53.345171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.389172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.255233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.102101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:01.523310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.172392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:45.995909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.826003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.643412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.657799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:53.544133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.522140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.440436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.285803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:01.718196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.287071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:46.151040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:47.964141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.784041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.809828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:53.731365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.666760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.574699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.415128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:01.929007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.414113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:46.283946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:48.098936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:49.915971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:51.944991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:53.914180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.797634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.717822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.548467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:02.206212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.565301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:46.460879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:48.291631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:50.275263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:52.131597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:54.093869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:55.926681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:57.912429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.729357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:02.465037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.729307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:46.639035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:48.469290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:50.468909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:52.319218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:54.286431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:56.069935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:58.091692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:59.917811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:02.774024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:44.952389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:46.913034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:48.737584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:50.743145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:52.603451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:54.584735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:56.263673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:30:58.360905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T01:31:00.181769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T01:31:13.411411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ActiveCase_Fatality_RatioConfirmedDeathsFIPSIncident_RateLatLong_Recoveredletalidad
Active1.0000.1560.7870.730-0.1090.2200.0450.0230.0120.149
Case_Fatality_Ratio0.1561.0000.0430.341-0.0960.120-0.112-0.0060.0230.994
Confirmed0.7870.0431.0000.918-0.100-0.036-0.0590.3700.5170.040
Deaths0.7300.3410.9181.000-0.1460.006-0.0900.3250.4760.343
FIPS-0.109-0.096-0.100-0.1461.000-0.1120.0220.133NaN-0.103
Incident_Rate0.2200.120-0.0360.006-0.1121.0000.081-0.422-0.5210.114
Lat0.045-0.112-0.059-0.0900.0220.0811.000-0.173-0.131-0.111
Long_0.023-0.0060.3700.3250.133-0.422-0.1731.0000.7100.001
Recovered0.0120.0230.5170.476NaN-0.521-0.1310.7101.0000.019
letalidad0.1490.9940.0400.343-0.1030.114-0.1110.0010.0191.000

Missing values

2025-11-26T01:31:03.125961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T01:31:04.066644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T01:31:06.010525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_Ratioletalidad
0NaNNaNNaNAfghanistan2021-01-02 05:22:3333.9391167.70995352513220141727.08585.0Afghanistan134.8965784.1913434.191343
1NaNNaNNaNAlbania2021-01-02 05:22:3341.1533020.16830058316118133634.023501.0Albania2026.4090622.0251732.025173
2NaNNaNNaNAlgeria2021-01-02 05:22:3328.033901.65960099897276267395.029740.0Algeria227.8098612.7648482.764848
3NaNNaNNaNAndorra2021-01-02 05:22:3342.506301.5218008117847463.0570.0Andorra10505.4034821.0348651.034865
4NaNNaNNaNAngola2021-01-02 05:22:33-11.2027017.8739001756840511146.06017.0Angola53.4529812.3053282.305328
5NaNNaNNaNAntigua and Barbuda2021-01-02 05:22:3317.06080-61.7964001595148.06.0Antigua and Barbuda162.3641863.1446543.144654
6NaNNaNNaNArgentina2021-01-02 05:22:33-38.41610-63.6167001629594433191426676.0159599.0Argentina3605.6333322.6582692.658269
7NaNNaNNaNArmenia2021-01-02 05:22:3340.0691045.0382001597382828143355.013555.0Armenia5390.6643891.7703991.770399
8NaNNaNAustralian Capital TerritoryAustralia2021-01-02 05:22:33-35.47350149.0124001183114.01.0Australian Capital Territory, Australia27.5636532.5423732.542373
9NaNNaNNew South WalesAustralia2021-01-02 05:22:33-33.86880151.2093004947540.04893.0New South Wales, Australia60.9386551.0915711.091571
FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_Ratioletalidad
726488NaNNaNNaNWinter Olympics 20222021-07-01 04:21:1939.904200116.40740000.00.0Winter Olympics 20220.00.0<NA>
726489NaNNaNNaNAntarctica2021-07-01 04:21:19-71.94990023.34700000.00.0Antarctica0.00.0<NA>
726490NaNNaNJerseyUnited Kingdom2021-07-01 04:21:1949.213800-2.135803493690.00.0Jersey, United Kingdom0.00.01.975379
726491NaNNaNGuernseyUnited Kingdom2021-07-01 04:21:1949.448196-2.58949837170.00.0Guernsey, United Kingdom0.00.02.031063
726492NaNNaNNaNKorea, North2021-07-01 04:21:1940.339900127.51010000.00.0Korea, North0.00.0<NA>
726493NaNNaNUnknownUkraine2021-07-01 04:21:19NaNNaN000.00.0Unknown, Ukraine0.00.0<NA>
726494NaNNaNNaNNauru2021-07-01 04:21:19-0.522800166.93150000.00.0Nauru0.00.0<NA>
726495NaNNaNNiueNew Zealand2021-07-01 04:21:19-19.054400-169.86720000.00.0Niue, New Zealand0.00.0<NA>
726496NaNNaNNaNTuvalu2021-07-01 04:21:19-7.109500177.64930000.00.0Tuvalu0.00.0<NA>
726497NaNNaNPitcairn IslandsUnited Kingdom2021-07-01 04:21:19-24.376800-128.32420000.00.0Pitcairn Islands, United Kingdom0.00.0<NA>

Duplicate rows

Most frequently occurring

FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_Ratioletalidad# duplicates
73NaNNaNDiamond PrincessCanada2020-12-21 13:27:30NaNNaN010.00.0Diamond Princess, CanadaNaNNaNNaN181
74NaNNaNGrand PrincessCanada2020-12-21 13:27:30NaNNaN13013.00.0Grand Princess, CanadaNaN0.0000000.000000181
4580001.0Out of ALAlabamaUS2020-12-21 13:27:30NaNNaN00NaNNaNOut of AL, Alabama, USNaNNaNNaN116
5380023.0Out of MEMaineUS2020-08-07 22:34:20NaNNaN00NaNNaNOut of ME, Maine, USNaNNaNNaN116
5588888.0NaNDiamond PrincessUS2020-08-04 02:27:56NaNNaN490NaNNaNDiamond Princess, USNaN0.0000000.000000116
5790001.0UnassignedAlabamaUS2020-12-21 13:27:30NaNNaN00NaNNaNUnassigned, Alabama, USNaNNaNNaN116
6690032.0UnassignedNevadaUS2021-01-24 23:22:19NaNNaN00NaNNaNUnassigned, Nevada, USNaNNaNNaN116
6990051.0UnassignedVirginiaUS2020-12-21 13:27:30NaNNaN00NaNNaNUnassigned, Virginia, USNaNNaNNaN116
7199999.0NaNGrand PrincessUS2020-08-04 02:27:56NaNNaN1033NaNNaNGrand Princess, USNaN2.9126212.912621116
72NaNNaNBonaire, Sint Eustatius and SabaNetherlands2021-01-08 23:22:2712.1784-68.23851963180.013.0Bonaire, Sint Eustatius and Saba, Netherlands747.4924681.5306121.530612110